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10 9 12 14 6 13 12 13 6 12 10 9 12 7 10 11 15 10 12 10 12 11 11 12 15 12 11 9 11 11 9 15 12 9 12 12 9 9 11 12 12 12 12 6 11 12 9 11 9 10 10 9 12 11 9 9 12 6 10 12 11 14 8 9 10 10 10 11 10 12 14 10 8 8 7 11 6 9 12 12 12 9 15 15 13 9 12 9 15 11 11 6 14 11 8 10 10 9 8 9 10 11 14 12 9 13 8 12 14 9 10 12 12 9 9 12 15 12 11 8 11 11 10 12 9 11 15 14 6 9 9 8 7 10 6 9 9 7 11 9 12 9 10 11 7 12 8 13 11 11 12 11 12 3 10 13 10 6 11 12 9 10 15 9 6 9 15 15 9 11 9 11 10 9 6 12 13 12 12
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R Code
gp <- function(lambda, p) { (p^lambda-(1-p)^lambda)/lambda } sortx <- sort(x) c <- array(NA,dim=c(201)) for (i in 1:201) { if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx) } bitmap(file='test1.png') plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Distribution (lambda)',1,TRUE) a<-table.element(a,'Correlation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE) a<-table.element(a,c[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE) a<-table.element(a,(c[100]+c[102])/2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE) a<-table.element(a,c[115]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE) a<-table.element(a,c[151]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE) a<-table.element(a,c[201]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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